Self-powered circularly polarized light detector based on asymmetric chiral metamaterials

Circularly polarized light(CPL)has been given great attention because of its extensive application.While several devices for CPL detection have been studied,their performance is affected by the magnitude of photocurrent.In this paper,a self-powered photodetector based on hot electrons in chiral metamaterials is proposed and optimized.CPL can be distinguished by the direction of photocurrent without external bias owing to the interdigital electrodes with asymmetric chiral metamaterials.Distinguished by the direction of photocurrent,the device can easily detect the rotation direction of the CPL electric field,even if it only has a very weak responsivity.The responsivity of the proposed detector is near 1.9 mA/W at the wavelength of 1322 nm,which is enough to distinguish CPL.The detector we proposed has the potential for application in optical communication.

Sensor Robot Planning in Incomplete Environment

Aiming at the former formalized methods of robot planning should give the environment state, can not obtain the new knowledge of the environment. In order to improve the reason ability for obtaining new knowledge of the environment state, the actions in the process of planning such as external action and sensing action are formalized. A formalized reasoning method—CPNI (Colored Petri Net for Planning in incomplete environment) based on two kinds of actions is proposed, and the reasoning rule as Fluent Calculus in incomplete environment is applied. Robot planning experiment is modeled and simulated by using the tool CPNTools and the result shows the state knowledge of the door and the action sequence to reach the goal can be generated automatically in the CPNI net system.

High-impedance Fault Section Location for Distribution Networks Based on t-distributed Stochastic Neighbor Embedding and Variable Mode Decomposition

When high-impedance faults(HIFs)occur in resonant grounded distribution networks,the current that flows is extremely weak,and the noise interference caused by the distribution network operation and the sampling error of the measurement devices further masks the fault characteristics.Consequently,locating a fault section with high sensitivity is difficult.Unlike existing technologies,this study presents a novel fault feature identification framework that addresses this issue.The framework includes three key steps:(1)utilizing the variable mode decomposition(VMD)method to denoise the fault transient zero-sequence current(TZSC);(2)employing a manifold learning algorithm based on t-distributed stochastic neighbor embedding(t-SNE)to further reduce the redundant information of the TZSC after denoising and to visualize fault information in high-dimensional 2D space;and(3)classifying the signal of each measurement point based on the fuzzy clustering method and combining the network topology structure to determine the fault section location.Numerical simulations and field testing confirm that the proposed method accurately detects the fault location,even under the influence of strong noise interference.